I received my PhD in computer science from the University of Toronto in 2009.
After spending two post-doctoral years at MIT,
I joined the University of Toronto in 2011.
My research interests include
Probabilistic Graphical Models, and
Prospective students: Please
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Recent Research Highlights:
- See our recent Deep Learning Tutorial at KDD 2014: [Video], [ Slides].
- Check out our new website with demos and software.
- I am helping to run Thematic Program on Statistical Inference, Learning, and Models for Big Data at the Fields Institute.
- I am teaching an advanced Machine Learning course at the Fields Institute. Videos of my lectures will be available online. Also, check out Live Streaming of my course.
Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing
Yuri Burda, Roger B. Grosse, and Ruslan Salakhutdinov, 2014,
To appear in AI and Statistics, 2015 [arXiv]
Learning Generative Models with Visual Attention
Yichuan Tang, Nitish Srivastava, and Ruslan Salakhutdinov
Neural Information Processing Systems (NIPS 28), 2014, oral,
[ pdf ], Supplementary material [ pdf].
- A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov.
Neural Information Processing Systems (NIPS 28), 2014.
[ pdf ], Supplementary material [ zip].
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel, NIPS Deep Learning Workshop, 2014.
[ arXiv ], [ results ].
Multimodal Learning with Deep Boltzmann Machines
Nitish Srivastava and Ruslan Salakhutdinov
Journal of Machine Learning Research, 2014. [ pdf ]. Code is available [ here].
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov
Journal of Machine Learning Research, 2014. [ pdf].
Deep Learning for Neuroimaging: a Validation Study
S. Plis, D. Hjelm, R. Salakhutdinov, E. Allen, H. Bockholt, J. Long, H. Johnson, J. Paulsen, J. Turner, and V. Calhoun
Frontiers in Neuroscience, 2014. [ pdf].
Multi-task Neural Networks for QSAR Prediction
George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov, 2014.
Restricted Boltzmann Machines for Neuroimaging: An Application in Identifying Intrinsic Networks
Devon Hjelma, Vince Calhouna, Ruslan Salakhutdinov, Elena Allena, Tulay Adali, and Sergey Plisa
In NeuroImage, Volume 96, Aug 1 2014, pages 245 - 260. [ pdf].
Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel.
In 31th International Conference on Machine Learning (ICML 2014)
[pdf], [ Project Page].